Exploring the Unique Aspects of the Northern Social Economy of Food through a Complexity Lens
Bibliographic record
Abstract
First published advance online December 16, 2019This article explores our observations on the ways that a social economy of food emerges out of context and place in Northwestern Ontario. We use a theoretical approach that draws on concepts from complexity science to better understand how the diversity inherent in context and place enables the unique social, ecological, and economic features of four case study initiatives. Our analysis of these social economy of food case studies reveals areas where the social economy appears to function differently in Northwestern Ontario, and this divergence from the literature is the focus of the article. We suggest three unique processes: first, a blending of social and capitalist economies; second, limitations of the capitalist economy in this northern setting; and third, the impact of connections with the unique landscape of Northwestern Ontario. We see people in pursuit of livelihood and well-being who are connecting and interacting as complex systems, thereby adapting dynamically through feedback loops to their total ecosystem (social/economic and biophysical), and producing diverse economic and social benefits. The resulting diversity and innovation build well-being, adaptation, and resilience in Northwestern Ontario communities as local food initiatives are strengthened.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".